Biodiesel has emerged as a sustainable alternative fuel, particularly for compression ignition diesel engines, due to its renewable nature and reduced environmental footprint. Biodiesel combustion can raise NOₓ emissions, thus engine characteristics must be carefully optimized to meet emissions standards. Traditional approaches for predicting emissions are restricted since they are expensive, take a long time, and can’t accurately capture complicated nonlinear relationships. Machine Learning (ML) is a strong way to anticipate engine emissions in a variety of situations. This paper suggests employing a Random Forest (RF)-based regression framework to use experimental data to forecast important emission parameters including CO, HC, NOₓ, and CO₂ from biodiesel-powered engines. The RF model did a good job of showing how engine characteristics and emissions were related in a nonlinear way. The performance evaluation indicated that all types of emissions had strong R2 values (≥0.77), low Mean Squared Error (MSE), and acceptable Mean Absolute Percentage Error (MAPE). The residual plots shows that most of the data points on comparative scatter plots were within ±10% error margins. This study backs the use of ML to provide accurate, cheap, and scalable predictions about emissions in biodiesel engine applications.